Information on judgment invariance influences contributors' opting-in behavior in sequential collaboration

Sequential collaboration describes an aggregation process intensively researched for numerical judgments which is characterized by a first contributor creating a judgment that is subsequently adjusted or maintained by following contributors. In previous research, participants performing sequential collaboration were only provided with information about the judgment of the person immediately preceding them in a sequential chain. However, in real-world collaborative projects (e.g., Wikipedia, Google Docs projects), more information about the past development of a sequential chain is often accessible or even directly displayed. As a concise piece of such information, we used judgment invariance, that is, the number of times a current judgment remained unchanged in the immediately preceding steps of a sequential chain. We hypothesized that increasing judgment invariance decreases both the probability and the magnitude of participants' judgment changes. Additionally, we hypothesized that the influence would be weakened with increasing expertise of participants. In three preregistered experiments, (G)LMM analyses suggested that increasing judgment invariance decreased the probability and magnitude of judgment changes confirming our hypothesized main effects. Concerning the interaction hypothesis of judgment invariance and expertise, a more ambiguous picture emerged. Experiment 1 was completely consistent with the interaction hypothesis. Experiment 2 supported it concerning the probability but not the magnitude of participants' judgment changes. In Experiment 3, a directionally reversed interaction effect was observed, possibly due to unconscientious participation. We conclude that the insight into the past development of a sequential chain, specifically information on judgment invariance, influences the judgment behavior of contributors in sequential collaborations. In summary, judgment invariance could be established as a substantial influence in sequential collaboration, which comes with practical implications for real-world collaborative projects.